CN112732298A - Data processing method, intelligent robot and related product - Google Patents
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Abstract
The embodiment of the application discloses a data processing method, an intelligent robot and related products, which are applied to the intelligent robot, wherein the method comprises the following steps: obtaining an upgrading instruction, wherein the upgrading instruction carries an upgrading data packet; analyzing the upgrading data packet to obtain upgrading control parameters; and carrying out first upgrading operation on the intelligent robot according to the upgrading control parameters. By adopting the embodiment of the application, the upgrading efficiency can be improved.
Description
Technical Field
The application relates to the technical field of artificial intelligence, in particular to a data processing method, an intelligent robot and related products.
Background
The intelligent robot can understand human language and converse with the operator by human language, and separately forms an external environment-actual situation exhaustive mode for 'living' in self 'consciousness'. It can analyze the situation that occurs, can adjust its own actions to meet all the requirements set by the operator, can formulate the desired actions, and accomplish them under conditions of insufficient information and rapid environmental changes. Of course, this is impossible to do as it is thought by our human. However, there are still attempts to establish some kind of "micro-world" that computers can understand. In life, the intelligent robot also needs to learn continuously to improve the ability of the intelligent robot, so that the problem of how to realize the upgrading of the intelligent robot needs to be solved urgently.
Disclosure of Invention
The embodiment of the application provides a data processing method, an intelligent robot and related products, and the upgrading efficiency of the intelligent robot can be improved.
In a first aspect, an embodiment of the present application provides a data processing method, which is applied to an intelligent robot, and the method includes:
obtaining an upgrading instruction, wherein the upgrading instruction carries an upgrading data packet;
analyzing the upgrading data packet to obtain upgrading control parameters;
and carrying out first upgrading operation on the intelligent robot according to the upgrading control parameters.
Optionally, the analyzing the upgrade data packet to obtain an upgrade control parameter includes:
acquiring target identity information;
determining a target data identifier corresponding to the target identity information according to a preset mapping relation between the identity information and the data identifier;
determining target data corresponding to the target data identification from the upgrading data packet;
and determining the upgrading control parameters according to the target data.
Optionally, the determining the upgrade control parameter according to the target data includes:
performing feature extraction on the target data to obtain a target feature set;
inputting the target feature set into a preset neural network model to obtain a target operation result;
and determining the upgrading control parameters corresponding to the target operation result according to a preset mapping relation between the operation result and the control parameters.
Optionally, before the obtaining the upgrade instruction, the method further includes:
acquiring target voice information;
converting the target voice information into text information;
extracting keywords from the text information to obtain target keywords;
and when the target keyword is a preset keyword, executing the step of acquiring the upgrading instruction.
Optionally, before the obtaining the upgrade instruction, the method further includes:
acquiring the last upgrading time;
determining a target time interval between a current time and the upgrade time;
when the target time interval is within a preset range, acquiring a target operation data volume of the intelligent robot between the current time and the upgrading time;
acquiring a reference operation data volume;
determining a target adjusting coefficient according to the reference operation data amount and the target operation data amount;
determining the upgrading time according to the target adjusting coefficient and the last upgrading time;
and when the upgrading time is up, executing the upgrading instruction acquisition.
Optionally, after the first upgrade operation is performed on the intelligent robot according to the upgrade control parameter, the method further includes:
acquiring a target operation instruction;
determining a target integral corresponding to the target operation instruction;
and carrying out second upgrading operation on the intelligent robot according to the target integral.
In a second aspect, an embodiment of the present application provides a data processing apparatus, which is applied to an intelligent robot, and the apparatus includes: an acquisition unit, a parsing unit and an upgrade unit, wherein,
the obtaining unit is used for obtaining an upgrading instruction, and the upgrading instruction carries an upgrading data packet;
the analysis unit is used for analyzing the upgrading data packet to obtain upgrading control parameters;
and the upgrading unit is used for carrying out first upgrading operation on the intelligent robot according to the upgrading control parameters.
Optionally, in the aspect of analyzing the upgrade data packet to obtain the upgrade control parameter, the analyzing unit is specifically configured to:
acquiring target identity information;
determining a target data identifier corresponding to the target identity information according to a preset mapping relation between the identity information and the data identifier;
determining target data corresponding to the target data identification from the upgrading data packet;
and determining the upgrading control parameters according to the target data.
In a third aspect, the present application provides an intelligent robot, including a processor, a memory, a communication interface, and one or more programs, where the one or more programs are stored in the memory and configured to be executed by the processor, and the program includes instructions for executing the steps in the first aspect of the present application.
In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program for electronic data exchange, where the computer program enables a computer to perform some or all of the steps described in the first aspect of the embodiment of the present application.
In a fifth aspect, embodiments of the present application provide a computer program product, where the computer program product includes a non-transitory computer-readable storage medium storing a computer program, where the computer program is operable to cause a computer to perform some or all of the steps as described in the first aspect of the embodiments of the present application. The computer program product may be a software installation package.
The embodiment of the application has the following beneficial effects:
it can be seen that the data processing method, the intelligent robot and the related products described in the embodiments of the present application are applied to an intelligent robot, obtain an upgrade instruction, the upgrade instruction carries an upgrade data packet, parse the upgrade data packet to obtain upgrade control parameters, perform a first upgrade operation on the intelligent robot according to the upgrade control parameters, and can parse corresponding upgrade control parameters according to the upgrade packet to achieve targeted upgrade of the intelligent robot, which is beneficial to improving upgrade efficiency.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1A is a schematic structural diagram of an intelligent robot provided in an embodiment of the present application;
fig. 1B is a schematic flowchart of a data processing method according to an embodiment of the present application;
FIG. 2 is a schematic flow chart diagram of another data processing method provided in the embodiments of the present application;
FIG. 3 is a schematic structural diagram of another intelligent robot provided in an embodiment of the present application;
fig. 4 is a block diagram of functional units of a data processing apparatus according to an embodiment of the present application.
Detailed Description
The terms "first," "second," and the like in the description and claims of the present application and in the above-described drawings are used for distinguishing between different objects and not for describing a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
In order to make the technical solutions of the present application better understood, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The intelligent robot related to the embodiment of the application may be an Automated Guided Vehicle (AGV) robot, and taking an AGV car as an example, the AGV may refer to a transport Vehicle equipped with an electromagnetic or optical automatic navigation device, capable of traveling along a prescribed navigation path, and having safety protection and various transfer functions. The industrial application does not need a driver's transport vehicle, and a rechargeable storage battery is used as a power source of the industrial application. The traveling path and behavior can be controlled by a computer, or the traveling path can be established by an electromagnetic track (electromagnetic track-following system), the electromagnetic track is adhered to the floor, and the unmanned transportation vehicle moves and acts according to the information brought by the electromagnetic track.
As shown in fig. 1A, fig. 1A is a schematic structural diagram of an intelligent robot provided in an embodiment of the present application. The intelligent robot comprises a processor, a Memory, a signal processor, a transceiver, a display screen, a loudspeaker, a microphone, a Random Access Memory (RAM), a camera, a sensor, a network module and the like. The memory, the DSP, the loudspeaker, the microphone, the RAM, the camera, the sensor and the network module are connected with the processor, and the transceiver is connected with the signal processor.
The Processor is a control center of the intelligent robot, connects each part of the whole intelligent robot by using various interfaces and lines, executes various functions and Processing data of the intelligent robot by running or executing software programs and/or modules stored in the memory and calling data stored in the memory, thereby performing overall monitoring on the intelligent robot, and can be a Central Processing Unit (CPU), a Graphics Processing Unit (GPU) or a Network Processing Unit (NPU).
Further, the processor may integrate an application processor, which mainly handles operating systems, user interfaces, application programs, etc., and a modem processor, which mainly handles wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor.
The memory is used for storing software programs and/or modules, and the processor executes various functional applications and data processing of the intelligent robot by running the software programs and/or modules stored in the memory. The memory mainly comprises a program storage area and a data storage area, wherein the program storage area can store an operating system, a software program required by at least one function and the like; the storage data area may store data created according to the use of the intelligent robot, and the like. Further, the memory may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device.
Wherein the sensor comprises at least one of: light-sensitive sensors, gyroscopes, infrared proximity sensors, vibration detection sensors, pressure sensors, etc. Among them, the light sensor, also called an ambient light sensor, is used to detect the ambient light brightness. The light sensor may include a light sensitive element and an analog to digital converter. The photosensitive element is used for converting collected optical signals into electric signals, and the analog-to-digital converter is used for converting the electric signals into digital signals. Optionally, the light sensor may further include a signal amplifier, and the signal amplifier may amplify the electrical signal converted by the photosensitive element and output the amplified electrical signal to the analog-to-digital converter. The photosensitive element may include at least one of a photodiode, a phototransistor, a photoresistor, and a silicon photocell.
The camera may be a visible light camera (general view angle camera, wide angle camera), an infrared camera, or a dual camera (having a distance measurement function), which is not limited herein.
The network module may be at least one of: a bluetooth module, a wireless fidelity (Wi-Fi), etc., which are not limited herein.
Based on the intelligent robot described in fig. 1A, the following data processing method can be executed, and the specific steps are as follows:
obtaining an upgrading instruction, wherein the upgrading instruction carries an upgrading data packet;
analyzing the upgrading data packet to obtain upgrading control parameters;
and carrying out first upgrading operation on the intelligent robot according to the upgrading control parameters.
It can be seen that, the intelligent robot described in the embodiment of the present application obtains the upgrade instruction, and the upgrade instruction carries the upgrade data packet, and parses the upgrade data packet, to obtain the upgrade control parameter, and performs the first upgrade operation on the intelligent robot according to the upgrade control parameter, and can parse the corresponding upgrade control parameter according to the upgrade packet, so as to implement targeted upgrade on the intelligent robot, and contribute to improving the upgrade efficiency.
Referring to fig. 1B, fig. 1B is a schematic flowchart of a data processing method according to an embodiment of the present application, and as shown in the drawing, the data processing method is applied to the intelligent robot shown in fig. 1A, and includes:
101. and obtaining an upgrading instruction, wherein the upgrading instruction carries an upgrading data packet.
The intelligent robot can receive an upgrading instruction, and the upgrading instruction can be triggered by a user or come from a server. The upgrade data package may include user habit data, user action data, or an algorithm upgrade package from the server. The user habit data may be at least one of: usage function, usage frequency, usage time, usage area, etc., without limitation. The user operation data may be at least one of: the operation function, the operation position, the operation time, the operation frequency, the operation mode, the touch parameter, and the like, which are not limited herein.
In specific implementation, the intelligent robot may include an upgrade control or a physical key, when the upgrade touch is pressed by the touch or the physical key, the intelligent robot may obtain an upgrade instruction, and at this time, the operation data in a period of time may be used as an upgrade data packet to implement upgrade operation on the intelligent robot, so as to improve the system performance of the intelligent robot. Or the intelligent robot can establish communication with the server, and further can receive an upgrading instruction of the server, wherein the upgrading instruction carries an upgrading data packet.
102. And analyzing the upgrading data packet to obtain upgrading control parameters.
In this embodiment of the present application, the upgrade control parameter may be at least one of the following: upgrade time, upgrade software modules, upgrade rate, upgrade program type, etc., without limitation.
In specific implementation, the intelligent robot may analyze the upgrade data packet to obtain corresponding upgrade control parameters, for example, to implement personalized upgrade for the characteristics of the intelligent robot itself, and for example, may also implement comprehensive upgrade for the intelligent robot.
In one possible example, in the step 102, parsing the upgrade data packet to obtain the upgrade control parameter may include the following steps:
21. acquiring target identity information;
22. determining a target data identifier corresponding to the target identity information according to a preset mapping relation between the identity information and the data identifier;
23. determining target data corresponding to the target data identification from the upgrading data packet;
24. and determining the upgrading control parameters according to the target data.
The intelligent robot can pre-store a mapping relation between preset identity information and data identifications, wherein different data identifications correspond to different data, data types, data positions or data functions and the like. In this embodiment of the application, the target identity information may include identity information of the intelligent robot, or identity information of the user, and the identity information of the intelligent robot may be at least one of the following: the model of the intelligent robot, the version number of the intelligent robot, the configuration information of the intelligent robot, etc., which is not limited herein, the configuration information of the intelligent robot may include at least one of the following: CPU resource usage, CPU model, GPU resource usage, GPU model, NPU resource usage, NPU model, memory size, and the like, without limitation. The identity information of the user may be at least one of: user name, user age, face image, iris image, vein image, fingerprint image, voice print information, user nationality, user occupation, user rating, character representation, and the like, without limitation.
In the specific implementation, the intelligent robot can obtain target identity information, further, according to a mapping relation between preset identity information and data identification, a target data identification corresponding to the target identity information is determined, further, target data corresponding to the target data identification can be determined from an upgrade data packet, in this way, data required by the robot or a user can be obtained, upgrade control parameters can be determined according to the target data, for example, different data represent different upgrade positions (software), further, upgrading can be achieved according to product attributes or user requirements of the intelligent robot, namely, personalized upgrading is achieved, personalized requirements of the user are met, upgrading efficiency is improved, the intelligent robot is upgraded towards the user requirements, and user experience is improved.
Further, in a possible example, the step 24 of determining the upgrade control parameter according to the target data may include the following steps:
241. performing feature extraction on the target data to obtain a target feature set;
242. inputting the target feature set into a preset neural network model to obtain a target operation result;
243. and determining the upgrading control parameters corresponding to the target operation result according to a preset mapping relation between the operation result and the control parameters.
The preset neural network model can be used for a user to train the intelligent robot, and in the embodiment of the application, the preset neural network model can be at least one of the following models: a recurrent neural network model, a convolutional neural network model, a fully-connected neural network model, a spiking neural network model, etc., without limitation. The intelligent robot can pre-store the mapping relation between the preset operation result and the control parameter.
In a specific implementation, the intelligent robot may perform feature extraction on target data to obtain a target feature set, where the target feature set may include one or more features, and the features may be at least one of the following: feature points, feature contours, feature lines, keywords, feature vectors, and the like, which are not limited herein. Furthermore, the intelligent robot may input the target feature set into a preset neural network model to obtain a target operation result, in this embodiment, the operation result may be a probability value, or a tag, and the tag may be used to indicate upgrade information, where the upgrade information may be at least one of: upgrade rate, upgrade location, upgrade time, etc., without limitation. Furthermore, the intelligent robot can determine the upgrading control parameters corresponding to the target operation results according to the preset mapping relation between the operation results and the control parameters, so that the upgrading information of the intelligent robot can be determined according to the data in the upgrading packet, the upgrading efficiency is improved, of course, the upgrading data packet can also comprise user habit data, and then the upgraded intelligent robot is more suitable for user habits and is beneficial to improving user experience.
103. And carrying out first upgrading operation on the intelligent robot according to the upgrading control parameters.
In concrete implementation, the intelligent robot can upgrade the corresponding functions of the intelligent robot according to the upgrade control parameters, and the upgraded intelligent robot is more intelligent.
In one possible example, before obtaining the upgrade instruction in step 101, the following steps may be further included:
a1, acquiring target voice information;
a2, converting the target voice information into text information;
a3, extracting keywords from the text information to obtain target keywords;
and A4, when the target keyword is a preset keyword, executing the step of acquiring the upgrading instruction.
The preset keywords can be set by the user or defaulted by the system. In the concrete implementation, the intelligent robot can obtain the target voice information, then, the target voice information can be converted into the text information by adopting a voice recognition mode, keywords can be extracted from the text information to obtain the target keywords, when the target keywords are preset keywords, the fact that the user wants to upgrade the intelligent robot is shown, the intelligent robot can execute the step of obtaining the upgrade instruction, and therefore, the upgrade is realized by adopting a voice mode, and the upgrade efficiency is improved.
In one possible example, the step a3, performing keyword extraction on the text information to obtain the target keyword, may include the following steps:
a31, extracting keywords from the text information to obtain a plurality of keywords;
a32, determining the criticality of each keyword in the plurality of keywords to obtain a plurality of criticalities;
and A33, determining the maximum value of the plurality of key degrees, and acquiring the target key words corresponding to the maximum value.
The intelligent robot can be used for storing the key words and the key degrees, wherein different key words can correspond to different key degrees, the mapping relation between the key words and the key degrees can be stored in the intelligent robot in advance, and the higher the key degree is, the more the key degree is. Furthermore, the intelligent robot can determine the key degree of each key word in the plurality of key words to obtain a plurality of key degrees, and can also determine the maximum value in the plurality of key degrees and obtain the target key word corresponding to the maximum value.
Further, in a possible example, the step 31 of determining the criticality of each keyword in the plurality of keywords to obtain a plurality of criticalities may include the following steps:
311. determining a target position of a keyword a in the text message and a reference keyword level of the keyword a, wherein the keyword a is any keyword in the plurality of keywords;
312. determining a reference criticality corresponding to the reference criticality according to a mapping relation between a preset criticality grade and the criticality;
313. determining a target first optimization factor corresponding to the target position according to a mapping relation between a preset position and the first optimization factor;
314. acquiring a volume parameter of the keyword a;
315. determining a target second optimization factor corresponding to the volume parameter of the keyword a according to a mapping relation between a preset volume parameter and the second optimization factor;
316. and optimizing the reference criticality according to the target first optimization factor and the target second optimization factor to obtain the criticality of the keyword a.
The preset threshold may be set by the user or default by the system, and may be an empirical value. The intelligent robot may pre-store a mapping relationship between a preset key level and a key degree, a mapping relationship between a preset position and a first optimization factor, and a mapping relationship between a preset volume parameter and a second optimization factor.
In the specific implementation, taking the keyword a as an example, the keyword a is any keyword of a plurality of keywords, the intelligent robot may determine a target position of the keyword a in the text information and a reference keyword level of the keyword a, determine a reference keyword corresponding to the reference keyword level according to a mapping relationship between a preset keyword level and the keyword level, determine a target first optimization factor corresponding to the target position according to a mapping relationship between a preset position and the first optimization factor, where a value range of the first optimization factor may be-1 to 1, and for example, the first optimization factor may be-0.08 to 0.08.
Further, because the volume parameters of the keywords are different, it is indicated that the attention degrees of the users are different, the volume parameters may be volume or tone, a target second optimization factor corresponding to the volume parameter of the keyword a is determined according to a mapping relationship between a preset volume parameter and the second optimization factor, a value range of the second optimization factor may be-1 to 1, for example, the second optimization factor may be-0.032 to 0.032, and the reference criticality is adjusted according to the target first optimization factor and the target second optimization factor to obtain the criticality of the keyword a, and the specific calculation formula is as follows:
the key degree of the keyword a is the reference key degree of the keyword a (1+ target first optimization factor) ((1 + target second optimization factor))
Furthermore, the key degree of the keyword can be accurately determined according to the position of the keyword and the volume parameter, and the command identification precision is improved.
In one possible example, before obtaining the upgrade instruction in step 101, the following steps may be further included:
b1, acquiring the last upgrading time;
b2, determining a target time interval between the current time and the upgrading time;
b3, when the target time interval is in a preset range, acquiring a target operation data volume of the intelligent robot between the current time and the upgrading time;
b4, acquiring a reference operation data volume;
b5, determining a target adjusting coefficient according to the reference operation data amount and the target operation data amount;
b6, determining the upgrade time according to the target adjustment coefficient and the last upgrade time;
and B7, when the upgrade time is up, executing the upgrade acquiring instruction.
Wherein, the preset range can be set by the user or the default of the system.
In specific implementation, the intelligent robot may obtain last upgrade time and determine a target time interval between current time and the upgrade time, and when the target time interval is within a preset range, obtain a target operation data amount of the intelligent robot between the current time and the upgrade time. Furthermore, the intelligent robot may determine the target adjustment coefficient according to the reference operation data amount and the target operation data amount, and the specific calculation formula is as follows:
target adjustment coefficient ═ target operation data amount-reference operation data amount/reference operation data amount
Furthermore, the intelligent robot may determine the upgrade time according to the target adjustment coefficient and the target time interval, for example, the upgrade time is (1+ target adjustment coefficient) the last upgrade time, and then when the upgrade time arrives, the upgrade instruction is obtained.
Further, in a possible example, before obtaining the last upgrade time in step B1, the following steps may be further included:
c1, determining the target operation times of the intelligent robot;
and C2, when the target operation times is more than the preset times, executing the step of obtaining the last upgrading time.
The preset times can be set by the user or the default of the system. In specific implementation, the intelligent robot can determine the target operation times of the intelligent robot, and when the target operation times is greater than the preset times, the step of obtaining the last upgrade time is executed, otherwise, the update is temporarily not needed, so that the update can be realized in a certain operation times, the performance of equipment is favorably improved, and the intelligence of the intelligent robot can be improved.
In one possible example, after performing a first upgrade operation on the intelligent robot according to the upgrade control parameters in step 103, the following steps may be further included:
d1, acquiring a target operation instruction;
d2, determining a target integral corresponding to the target operation instruction;
d3, performing a second upgrading operation on the intelligent robot according to the target integral.
The target operation instruction may be an instruction for a user to instruct the intelligent robot to execute an action required by the user, and the target operation instruction may be at least one of the following: a water taking instruction, a meal delivery instruction, a reminder instruction, a dancing instruction, a projection instruction, a search instruction, etc., without limitation. Different instructions can correspond to different integrals, the mapping relation between the operation instruction and the integrals can be prestored in the intelligent robot, further, the target integral corresponding to the target operation instruction can be determined according to the mapping relation, the current integral can also be obtained, further, after the target operation instruction is completed, the sum of the current integral and the target integral can be used as the updated integral, when the integral reaches the upgrading operation, the second upgrading operation can be realized, which is equivalent to the improvement of the integral or the growth value of the intelligent robot per se, the integral or the growth value can reflect the preference degree of a user to the intelligent robot, of course, the background (cloud end) can also obtain the upgrading data (the integral or the growth value) of the intelligent robot for analyzing the preferred intelligent robot behavior of the user, and further, the research and development of the intelligent robot can be completed in a more targeted manner, the behavior of the intelligent robot is developed or deeply developed according to the preference of the user, so that the intelligent robot can serve the user better.
For example, taking the example of an AGV Intelligent robot, the AGV may perform various operations and skills, such as taking water, delivering meals, alerting the owner that he is asleep, and so forth. As the robot grows, the AGV can perform more operations. And the background updates the information of the AGV in real time, issues a new growth instruction, and instructs an administrator to execute the operation of the growth instruction, so that the AGV is continuously updated and grown.
It can be seen that the data processing method described in the embodiment of the present application is applied to an intelligent robot, obtains an upgrade instruction, the upgrade instruction carries an upgrade data packet, analyzes the upgrade data packet to obtain an upgrade control parameter, performs a first upgrade operation on the intelligent robot according to the upgrade control parameter, and can analyze a corresponding upgrade control parameter according to the upgrade packet, so as to implement targeted upgrade on the intelligent robot, and is helpful for improving upgrade efficiency.
Referring to fig. 2, in accordance with the embodiment shown in fig. 1B, fig. 2 is a schematic flowchart of a data processing method provided in an embodiment of the present application, and the data processing method is applied to the intelligent robot shown in fig. 1A, and the data processing method includes:
201. and acquiring target voice information.
202. And converting the target voice information into text information.
203. And extracting keywords from the text information to obtain target keywords.
204. And when the target keyword is a preset keyword, acquiring an upgrading instruction, wherein the upgrading instruction carries an upgrading data packet.
205. And analyzing the upgrading data packet to obtain upgrading control parameters.
206. And carrying out first upgrading operation on the intelligent robot according to the upgrading control parameters.
For the detailed description of the steps 201 to 206, reference may be made to corresponding steps of the data processing method described in the foregoing fig. 1B, and details are not repeated here.
It can be seen that the data processing method described in the embodiment of the present application is applied to an intelligent robot, obtains target voice information, converts the target voice information into text information, performs keyword extraction on the text information to obtain target keywords, obtains an upgrade instruction when the target keywords are preset keywords, and the upgrade instruction carries an upgrade data packet and parses the upgrade data packet to obtain upgrade control parameters.
Referring to fig. 3 in keeping with the above embodiments, fig. 3 is a schematic structural diagram of an intelligent robot according to an embodiment of the present application, and as shown in the drawing, the intelligent robot includes a processor, a memory, a communication interface, and one or more programs, where the one or more programs are stored in the memory and configured to be executed by the processor, and in an embodiment of the present application, the programs include instructions for performing the following steps:
obtaining an upgrading instruction, wherein the upgrading instruction carries an upgrading data packet;
analyzing the upgrading data packet to obtain upgrading control parameters;
and carrying out first upgrading operation on the intelligent robot according to the upgrading control parameters.
It can be seen that, the intelligent robot described in the embodiment of the present application obtains the upgrade instruction, and the upgrade instruction carries the upgrade data packet, and parses the upgrade data packet, to obtain the upgrade control parameter, and performs the first upgrade operation on the intelligent robot according to the upgrade control parameter, and can parse the corresponding upgrade control parameter according to the upgrade packet, so as to implement targeted upgrade on the intelligent robot, and contribute to improving the upgrade efficiency.
In one possible example, in the parsing the upgrade data packet to obtain upgrade control parameters, the program includes instructions for performing the following steps:
acquiring target identity information;
determining a target data identifier corresponding to the target identity information according to a preset mapping relation between the identity information and the data identifier;
determining target data corresponding to the target data identification from the upgrading data packet;
and determining the upgrading control parameters according to the target data.
Further, in one possible example, in said determining said upgrade control parameter in dependence on said target data, the above program comprises instructions for performing the steps of:
performing feature extraction on the target data to obtain a target feature set;
inputting the target feature set into a preset neural network model to obtain a target operation result;
and determining the upgrading control parameters corresponding to the target operation result according to a preset mapping relation between the operation result and the control parameters.
In one possible example, prior to said obtaining upgrade instructions, the program further comprises instructions for:
acquiring target voice information;
converting the target voice information into text information;
extracting keywords from the text information to obtain target keywords;
and when the target keyword is a preset keyword, executing the step of acquiring the upgrading instruction.
In one possible example, prior to said obtaining upgrade instructions, the program further comprises instructions for:
acquiring the last upgrading time;
determining a target time interval between a current time and the upgrade time;
when the target time interval is within a preset range, acquiring a target operation data volume of the intelligent robot between the current time and the upgrading time;
acquiring a reference operation data volume;
determining a target adjusting coefficient according to the reference operation data amount and the target operation data amount;
determining the upgrading time according to the target adjusting coefficient and the last upgrading time;
and when the upgrading time is up, executing the upgrading instruction acquisition.
In one possible example, after the first upgrade operation of the intelligent robot according to the upgrade control parameters, the program further includes instructions for:
acquiring a target operation instruction;
determining a target integral corresponding to the target operation instruction;
and carrying out second upgrading operation on the intelligent robot according to the target integral.
The above description has introduced the solution of the embodiment of the present application mainly from the perspective of the method-side implementation process. It is understood that in order to implement the above functions, it includes corresponding hardware structures and/or software modules for performing the respective functions. Those of skill in the art will readily appreciate that the present application is capable of hardware or a combination of hardware and computer software implementing the various illustrative elements and algorithm steps described in connection with the embodiments provided herein. Whether a function is performed as hardware or computer software drives hardware depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiment of the present application, the functional units may be divided according to the above method example, for example, each functional unit may be divided corresponding to each function, or two or more functions may be integrated into one processing unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit. It should be noted that the division of the unit in the embodiment of the present application is schematic, and is only a logic function division, and there may be another division manner in actual implementation.
Fig. 4 is a block diagram of functional units of a data processing apparatus 400 according to an embodiment of the present application, where the apparatus 400 is applied to an intelligent robot, and the apparatus 400 includes: an acquisition unit 401, a parsing unit 402, and an upgrade unit 403, wherein,
the obtaining unit 401 is configured to obtain an upgrade instruction, where the upgrade instruction carries an upgrade data packet;
the analyzing unit 402 is configured to analyze the upgrade data packet to obtain an upgrade control parameter;
the upgrading unit 403 is configured to perform a first upgrading operation on the intelligent robot according to the upgrading control parameter.
It can be seen that, the data processing apparatus described in the embodiment of the present application is applied to an intelligent robot, acquires an upgrade instruction, and the upgrade instruction carries an upgrade data packet, and parses the upgrade data packet to obtain an upgrade control parameter, and performs a first upgrade operation on the intelligent robot according to the upgrade control parameter, and can parse out a corresponding upgrade control parameter according to the upgrade packet, so as to realize targeted upgrade of the intelligent robot, and contribute to improving upgrade efficiency.
In a possible example, in terms of parsing the upgrade data packet to obtain upgrade control parameters, the parsing unit 402 is specifically configured to:
acquiring target identity information;
determining a target data identifier corresponding to the target identity information according to a preset mapping relation between the identity information and the data identifier;
determining target data corresponding to the target data identification from the upgrading data packet;
and determining the upgrading control parameters according to the target data.
Further, in a possible example, in the aspect of determining the upgrade control parameter according to the target data, the parsing unit 402 is specifically configured to:
performing feature extraction on the target data to obtain a target feature set;
inputting the target feature set into a preset neural network model to obtain a target operation result;
and determining the upgrading control parameters corresponding to the target operation result according to a preset mapping relation between the operation result and the control parameters.
Further, in one possible example, before the obtaining the upgrade instructions, the apparatus 400 is further specifically configured to:
acquiring target voice information;
converting the target voice information into text information;
extracting keywords from the text information to obtain target keywords;
and when the target keyword is a preset keyword, executing the step of acquiring the upgrading instruction.
Further, in one possible example, before the obtaining the upgrade instructions, the apparatus 400 is further specifically configured to:
acquiring the last upgrading time;
determining a target time interval between a current time and the upgrade time;
when the target time interval is within a preset range, acquiring a target operation data volume of the intelligent robot between the current time and the upgrading time;
acquiring a reference operation data volume;
determining a target adjusting coefficient according to the reference operation data amount and the target operation data amount;
determining the upgrading time according to the target adjusting coefficient and the last upgrading time;
and when the upgrading time is up, executing the upgrading instruction acquisition.
In one possible example, after the first upgrade operation of the intelligent robot according to the upgrade control parameters, the apparatus 400 is further specifically configured to:
acquiring a target operation instruction;
determining a target integral corresponding to the target operation instruction;
and carrying out second upgrading operation on the intelligent robot according to the target integral.
It is to be understood that the functions of each program module of the data processing apparatus in this embodiment may be specifically implemented according to the method in the foregoing method embodiment, and the specific implementation process may refer to the relevant description of the foregoing method embodiment, which is not described herein again.
Embodiments of the present application also provide a computer storage medium, wherein the computer storage medium stores a computer program for electronic data exchange, the computer program enables a computer to execute part or all of the steps of any one of the methods as described in the above method embodiments, and the computer includes an intelligent robot.
Embodiments of the present application also provide a computer program product comprising a non-transitory computer readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps of any of the methods as described in the above method embodiments. The computer program product may be a software installation package, said computer comprising an intelligent robot.
It should be noted that, for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present application is not limited by the order of acts described, as some steps may occur in other orders or concurrently depending on the application. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required in this application.
In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus may be implemented in other manners. For example, the above-described embodiments of the apparatus are merely illustrative, and for example, the above-described division of the units is only one type of division of logical functions, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of some interfaces, devices or units, and may be an electric or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit may be stored in a computer readable memory if it is implemented in the form of a software functional unit and sold or used as a stand-alone product. Based on such understanding, the technical solution of the present application may be substantially implemented or a part of or all or part of the technical solution contributing to the prior art may be embodied in the form of a software product stored in a memory, and including several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the above-mentioned method of the embodiments of the present application. And the aforementioned memory comprises: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by associated hardware instructed by a program, which may be stored in a computer-readable memory, which may include: flash Memory disks, Read-Only memories (ROMs), Random Access Memories (RAMs), magnetic or optical disks, and the like.
The foregoing detailed description of the embodiments of the present application has been presented to illustrate the principles and implementations of the present application, and the above description of the embodiments is only provided to help understand the method and the core concept of the present application; meanwhile, for a person skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.
Claims (10)
1. A data processing method is applied to an intelligent robot, and the method comprises the following steps:
obtaining an upgrading instruction, wherein the upgrading instruction carries an upgrading data packet;
analyzing the upgrading data packet to obtain upgrading control parameters;
and carrying out first upgrading operation on the intelligent robot according to the upgrading control parameters.
2. The method of claim 1, wherein parsing the upgrade data packet to obtain upgrade control parameters comprises:
acquiring target identity information;
determining a target data identifier corresponding to the target identity information according to a preset mapping relation between the identity information and the data identifier;
determining target data corresponding to the target data identification from the upgrading data packet;
and determining the upgrading control parameters according to the target data.
3. The method of claim 2, wherein said determining said upgrade control parameters from said target data comprises:
performing feature extraction on the target data to obtain a target feature set;
inputting the target feature set into a preset neural network model to obtain a target operation result;
and determining the upgrading control parameters corresponding to the target operation result according to a preset mapping relation between the operation result and the control parameters.
4. The method of any of claims 1-3, wherein prior to said obtaining upgrade instructions, the method further comprises:
acquiring target voice information;
converting the target voice information into text information;
extracting keywords from the text information to obtain target keywords;
and when the target keyword is a preset keyword, executing the step of acquiring the upgrading instruction.
5. The method of any of claims 1-3, wherein prior to said obtaining upgrade instructions, the method further comprises:
acquiring the last upgrading time;
determining a target time interval between a current time and the upgrade time;
when the target time interval is within a preset range, acquiring a target operation data volume of the intelligent robot between the current time and the upgrading time;
acquiring a reference operation data volume;
determining a target adjusting coefficient according to the reference operation data amount and the target operation data amount;
determining the upgrading time according to the target adjusting coefficient and the last upgrading time;
and when the upgrading time is up, executing the upgrading instruction acquisition.
6. The method according to any one of claims 1-3, wherein after the first upgrade operation on the intelligent robot in accordance with the upgrade control parameters, the method further comprises:
acquiring a target operation instruction;
determining a target integral corresponding to the target operation instruction;
and carrying out second upgrading operation on the intelligent robot according to the target integral.
7. A data processing device, which is applied to an intelligent robot, the device comprising: an acquisition unit, a parsing unit and an upgrade unit, wherein,
the obtaining unit is used for obtaining an upgrading instruction, and the upgrading instruction carries an upgrading data packet;
the analysis unit is used for analyzing the upgrading data packet to obtain upgrading control parameters;
and the upgrading unit is used for carrying out first upgrading operation on the intelligent robot according to the upgrading control parameters.
8. The apparatus according to claim 7, wherein in the parsing the upgrade data packet to obtain the upgrade control parameter, the parsing unit is specifically configured to:
acquiring target identity information;
determining a target data identifier corresponding to the target identity information according to a preset mapping relation between the identity information and the data identifier;
determining target data corresponding to the target data identification from the upgrading data packet;
and determining the upgrading control parameters according to the target data.
9. An intelligent robot comprising a processor, a memory for storing one or more programs and configured for execution by the processor, the programs comprising instructions for performing the steps in the method of any of claims 1-6.
10. A computer-readable storage medium, characterized in that a computer program for electronic data exchange is stored, wherein the computer program causes a computer to perform the method according to any one of claims 1-6.
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